Systems and methods for altering a gui in response to in-session inferences

ABSTRACT

Systems and methods including one or more processors and one or more non-transitory computer readable storage devices storing computing instructions configured to run on the one or more processing modules and perform acts of: gathering historical data, which can comprise interactions of a user with a first graphical user interface at a first time; storing the historical data which can comprise the interactions of the user with the first graphical user interface as at least one historical feature vector; gathering in-session data, which can comprise interactions of the user with a second graphical user interface at a second time later than the first time; storing the in-session data which can comprise the interactions of the user with the second graphical user interface as at least one in-session feature vector; determining an intent of the user using the at least one historical feature vector and the at least one in-session feature vector; and transmitting instructions to display a third graphical user interface for the user based upon the intent of the user. Other embodiments are disclosed herein.

TECHNICAL FIELD

This disclosure relates generally to graphical user interfaces (GUIs),and more specifically relates to systems and methods for changing GUIsin response to an intent of a user.

BACKGROUND

Designing and altering graphical user interfaces (“GUIs”) based onlong-term views of user activity can have its limitations. It is oftenthe case that users have an established long-term GUI interactionpattern that can be discovered by looking at historical use, butfocusing strictly on a long-term use pattern can neglect the short-termintent that users are expressing. This situation can create aninefficient and un-optimized user experience that can be compounded ondevices with small screens, which are already difficult to navigate.Therefore, there is a need for a system and/or method for designingand/or altering GUIs that that (1) remains faithful to well-establishedtrends in user interaction, but (2) can quickly alter or synthesize aGUI based upon short-term, in-session behavior when an intent of a useris identified.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevational view of a computer system that issuitable for implementing various embodiments of the systems disclosedin FIGS. 3 and 6;

FIG. 2 illustrates a representative block diagram of an example of theelements included in the circuit boards inside a chassis of the computersystem of FIG. 1;

FIG. 3 illustrates a representative block diagram of a system, accordingto an embodiment;

FIG. 4 is a flowchart for a method, according to certain embodiments;

FIG. 5 is a flowchart for a method, according to certain embodiments;and

FIG. 6 illustrates a representative block diagram of a system, accordingto an additional embodiment.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques may be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures may be exaggeratedrelative to other elements to help improve understanding of embodimentsof the present disclosure. The same reference numerals in differentfigures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof, are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mayinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmay be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling may be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, two or more elements are “integral” if they arecomprised of the same piece of material. As defined herein, two or moreelements are “non-integral” if each is comprised of a different piece ofmaterial.

As defined herein, “real-time” can, in some embodiments, be defined withrespect to operations carried out as soon as practically possible uponoccurrence of a triggering event. A triggering event can include receiptof data necessary to execute a task or to otherwise process information.Because of delays inherent in transmission and/or in computing speeds,the term “real time” encompasses operations that occur in “near” realtime or somewhat delayed from a triggering event. In a number ofembodiments, “real time” can mean real time less a time delay forprocessing (e.g., determining) and/or transmitting data. The particulartime delay can vary depending on the type and/or amount of the data, theprocessing speeds of the hardware, the transmission capability of thecommunication hardware, the transmission distance, etc. However, in manyembodiments, the time delay can be less than approximately one second,two seconds, five seconds, or ten seconds.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

A number of embodiments can include a system. The system can include oneor more processors and one or more non-transitory storage devicescomputing instructions configured to run on the one or more processors.The computing instructions can be configured to run on the one or moreprocessors and perform acts of gathering historical data, which cancomprise interactions of a user with a first graphical user interface ata first time; storing the historical data which can comprise theinteractions of the user with the first graphical user interface as atleast one historical feature vector; gathering in-session data, whichcan comprise interactions of the user with a second graphical userinterface at a second time later than the first time; storing thein-session data which can comprise the interactions of the user with thesecond graphical user interface as at least one in-session featurevector; determining an intent of the user using the at least onehistorical feature vector and the at least one in-session featurevector; and transmitting instructions to display a third graphical userinterface for the user based upon the intent of the user

Various embodiments include a method. The method can include gatheringhistorical data, which can comprise interactions of a user with a firstgraphical user interface at a first time; storing the historical datawhich can comprise the interactions of the user with the first graphicaluser interface as at least one historical feature vector; gatheringin-session data, which can comprise interactions of the user with asecond graphical user interface at a second time later than the firsttime; storing the in-session data which can comprise the interactions ofthe user with the second graphical user interface as at least onein-session feature vector; determining an intent of the user using theat least one historical feature vector and the at least one in-sessionfeature vector; and transmitting instructions to display a thirdgraphical user interface for the user based upon the intent of the user

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor (i) implementing part or all of one or more embodiments of thetechniques, methods, and systems and/or (ii) implementing and/oroperating part or all of one or more embodiments of the memory storagemodules described herein. As an example, a different or separate one ofa chassis 102 (and its internal components) can be suitable forimplementing part or all of one or more embodiments of the techniques,methods, and/or systems described herein. Furthermore, one or moreelements of computer system 100 (e.g., a monitor 106, a keyboard 104,and/or a mouse 110, etc.) also can be appropriate for implementing partor all of one or more embodiments of the techniques, methods, and/orsystems described herein. Computer system 100 can comprise chassis 102containing one or more circuit boards (not shown), a Universal SerialBus (USB) port 112, a Compact Disc Read-Only Memory (CD-ROM) and/orDigital Video Disc (DVD) drive 116, and a hard drive 114. Arepresentative block diagram of the elements included on the circuitboards inside chassis 102 is shown in FIG. 2. A central processing unit(CPU) 210 in FIG. 2 is coupled to a system bus 214 in FIG. 2. In variousembodiments, the architecture of CPU 210 can be compliant with any of avariety of commercially distributed architecture families.

Continuing with FIG. 2, system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 can comprise (i)non-volatile memory, such as, for example, read only memory (ROM) and/or(ii) volatile memory, such as, for example, random access memory (RAM).The non-volatile memory can be removable and/or non-removablenon-volatile memory. Meanwhile, RAM can include dynamic RAM (DRAM),static RAM (SRAM), etc. Further, ROM can include mask-programmed ROM,programmable ROM (PROM), one-time programmable ROM (OTP), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM)and/or flash memory), etc. In these or other embodiments, memory storageunit 208 can comprise (i) non-transitory memory and/or (ii) transitorymemory.

In various examples, portions of the memory storage module(s) of thevarious embodiments disclosed herein (e.g., portions of the non-volatilememory storage module(s)) can be encoded with a boot code sequencesuitable for restoring computer system 100 (FIG. 1) to a functionalstate after a system reset. In addition, portions of the memory storagemodule(s) of the various embodiments disclosed herein (e.g., portions ofthe non-volatile memory storage module(s)) can comprise microcode suchas a Basic Input-Output System (BIOS) operable with computer system 100(FIG. 1). In the same or different examples, portions of the memorystorage module(s) of the various embodiments disclosed herein (e.g.,portions of the non-volatile memory storage module(s)) can comprise anoperating system, which can be a software program that manages thehardware and software resources of a computer and/or a computer network.The BIOS can initialize and test components of computer system 100(FIG. 1) and load the operating system. Meanwhile, the operating systemcan perform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files.Exemplary operating systems can comprise one of the following: (i)Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond,Wash., United States of America, (ii) Mac® OS X by Apple Inc. ofCupertino, Calif., United States of America, (iii) UNIX® OS, and (iv)Linux® OS. Further exemplary operating systems can comprise one of thefollowing: (i) the iOS® operating system by Apple Inc. of Cupertino,Calif., United States of America, (ii) the Blackberry® operating systemby Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) theWebOS operating system by LG Electronics of Seoul, South Korea, (iv) theAndroid™ operating system developed by Google, of Mountain View, Calif.,United States of America, (v) the Windows Mobile™ operating system byMicrosoft Corp. of Redmond, Wash., United States of America, or (vi) theSymbian™ operating system by Accenture PLC of Dublin, Ireland.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processing modules of thevarious embodiments disclosed herein can comprise CPU 210.

Alternatively, or in addition to, the systems and procedures describedherein can be implemented in hardware, or a combination of hardware,software, and/or firmware. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. For example, one or moreof the programs and/or executable program components described hereincan be implemented in one or more ASICs. In many embodiments, anapplication specific integrated circuit (ASIC) can comprise one or moreprocessors or microprocessors and/or memory blocks or memory storage.

In the depicted embodiment of FIG. 2, various I/O devices such as a diskcontroller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2) andmouse 110 (FIGS. 1-2), respectively, of computer system 100 (FIG. 1).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2, video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for monitor 106 (FIGS. 1-2) to display imageson a screen 108 (FIG. 1) of computer system 100 (FIG. 1). Diskcontroller 204 can control hard drive 114 (FIGS. 1-2), USB port 112(FIGS. 1-2), and CD-ROM drive 116 (FIGS. 1-2). In other embodiments,distinct units can be used to control each of these devices separately.

Network adapter 220 can be suitable to connect computer system 100(FIG. 1) to a computer network by wired communication (e.g., a wirednetwork adapter) and/or wireless communication (e.g., a wireless networkadapter). In some embodiments, network adapter 220 can be plugged orcoupled to an expansion port (not shown) in computer system 100 (FIG.1). In other embodiments, network adapter 220 can be built into computersystem 100 (FIG. 1). For example, network adapter 220 can be built intocomputer system 100 (FIG. 1) by being integrated into the motherboardchipset (not shown), or implemented via one or more dedicatedcommunication chips (not shown), connected through a PCI (peripheralcomponent interconnector) or a PCI express bus of computer system 100(FIG. 1) or USB port 112 (FIG. 1).

Returning now to FIG. 1, although many other components of computersystem 100 are not shown, such components and their interconnection arewell known to those of ordinary skill in the art. Accordingly, furtherdetails concerning the construction and composition of computer system100 and the circuit boards inside chassis 102 are not discussed herein.

Meanwhile, when computer system 100 is running, program instructions(e.g., computer instructions) stored on one or more of the memorystorage module(s) of the various embodiments disclosed herein can beexecuted by CPU 210 (FIG. 2). At least a portion of the programinstructions, stored on these devices, can be suitable for carrying outat least part of the techniques and methods described herein.

Further, although computer system 100 is illustrated as a desktopcomputer in FIG. 1, there can be examples where computer system 100 maytake a different form factor while still having functional elementssimilar to those described for computer system 100. In some embodiments,computer system 100 may comprise a single computer, a single server, ora cluster or collection of computers or servers, or a cloud of computersor servers. Typically, a cluster or collection of servers can be usedwhen the demand on computer system 100 exceeds the reasonable capabilityof a single server or computer. In certain embodiments, computer system100 may comprise a portable computer, such as a laptop computer. Incertain other embodiments, computer system 100 may comprise a mobileelectronic device, such as a smartphone. In certain additionalembodiments, computer system 100 may comprise an embedded system.

Turning ahead in the drawings, FIG. 3 illustrates a block diagram of asystem 300 that can be employed for altering user interfaces in responseto predicted user activity, as described in greater detail below. System300 is merely exemplary and embodiments of the system are not limited tothe embodiments presented herein. System 300 can be employed in manydifferent embodiments or examples not specifically depicted or describedherein. In some embodiments, certain elements or modules of system 300can perform various procedures, processes, and/or activities. In theseor other embodiments, the procedures, processes, and/or activities canbe performed by other suitable elements or modules of system 300.

Generally, therefore, system 300 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 300 described herein.

In some embodiments, system 300 can include a server computer 310,internet 320, user computers 340, 341, and/or GUI 330, 331. Servercomputer 310, internet 320, GUI 330, and/or user computer 340 can eachbe a computer system, such as computer system 100 (FIG. 1), as describedabove, and can each be a single computer, a single server, or a clusteror collection of computers or servers, or a cloud of computers orservers. In another embodiment, a single computer system can host eachof two or more of server computer 310 and/or internet 320. Additionaldetails regarding server computer 310, internet 320, GUI 330, and/oruser computer 340 are described herein.

In many embodiments, system 300 also can comprise user computers 340,341. User computers 340, 341 can comprise any of the elements describedin relation to computer system 100. In some embodiments, user computers340, 341 can be mobile devices. A mobile electronic device can refer toa portable electronic device (e.g., an electronic device easilyconveyable by hand by a person of average size) with the capability topresent audio and/or visual data (e.g., text, images, videos, music,etc.). For example, a mobile electronic device can comprise at least oneof a digital media player, a cellular telephone (e.g., a smartphone), apersonal digital assistant, a handheld digital computer device (e.g., atablet personal computer device), a laptop computer device (e.g., anotebook computer device, a netbook computer device), a wearable usercomputer device, or another portable computer device with the capabilityto present audio and/or visual data (e.g., images, videos, music, etc.).Thus, in many examples, a mobile electronic device can comprise a volumeand/or weight sufficiently small as to permit the mobile electronicdevice to be easily conveyable by hand. For examples, in someembodiments, a mobile electronic device can occupy a volume of less thanor equal to approximately 1790 cubic centimeters, 2434 cubiccentimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752cubic centimeters. Further, in these embodiments, a mobile electronicdevice can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3Newtons, 31.2 Newtons, and/or 44.5 Newtons.

Exemplary mobile electronic devices can comprise (i) an iPod®, iPhone®,iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino,Calif., United States of America, (ii) a Blackberry® or similar productby Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia®or similar product by the Nokia Corporation of Keilaniemi, Espoo,Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Groupof Samsung Town, Seoul, South Korea. Further, in the same or differentembodiments, a mobile electronic device can comprise an electronicdevice configured to implement one or more of (i) the iPhone® operatingsystem by Apple Inc. of Cupertino, Calif., United States of America,(ii) the Blackberry® operating system by Research In Motion (RIM) ofWaterloo, Ontario, Canada, (iii) the Palm® operating system by Palm,Inc. of Sunnyvale, Calif., United States, (iv) the Android™ operatingsystem developed by the Open Handset Alliance, (v) the Windows Mobile™operating system by Microsoft Corp. of Redmond, Wash., United States ofAmerica, or (vi) the Symbian™ operating system by Nokia Corp. ofKeilaniemi, Espoo, Finland.

Further still, the term “wearable user computer device” as used hereincan refer to an electronic device with the capability to present audioand/or visual data (e.g., text, images, videos, music, etc.) that isconfigured to be worn by a user and/or mountable (e.g., fixed) on theuser of the wearable user computer device (e.g., sometimes under or overclothing; and/or sometimes integrated with and/or as clothing and/oranother accessory, such as, for example, a hat, eyeglasses, a wristwatch, shoes, etc.). In many examples, a wearable user computer devicecan comprise a mobile electronic device, and vice versa. However, awearable user computer device does not necessarily comprise a mobileelectronic device, and vice versa.

In specific examples, a wearable user computer device can comprise ahead mountable wearable user computer device (e.g., one or more headmountable displays, one or more eyeglasses, one or more contact lenses,one or more retinal displays, etc.) or a limb mountable wearable usercomputer device (e.g., a smart watch). In these examples, a headmountable wearable user computer device can be mountable in closeproximity to one or both eyes of a user of the head mountable wearableuser computer device and/or vectored in alignment with a field of viewof the user.

In more specific examples, a head mountable wearable user computerdevice can comprise (i) Google Glass™ product or a similar product byGoogle Inc. of Menlo Park, Calif., United States of America; (ii) theEye Tap™ product, the Laser Eye Tap™ product, or a similar product byePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product,the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or asimilar product by Vuzix Corporation of Rochester, N.Y., United Statesof America. In other specific examples, a head mountable wearable usercomputer device can comprise the Virtual Retinal Display™ product, orsimilar product by the University of Washington of Seattle, Wash.,United States of America. Meanwhile, in further specific examples, alimb mountable wearable user computer device can comprise the iWatch™product, or similar product by Apple Inc. of Cupertino, Calif., UnitedStates of America, the Galaxy Gear or similar product of Samsung Groupof Samsung Town, Seoul, South Korea, the Moto 360 product or similarproduct of Motorola of Schaumburg, Ill., United States of America,and/or the Zip™ product, One™ product, Flex™ product, Charge™ product,Surge™ product, or similar product by Fitbit Inc. of San Francisco,Calif., United States of America.

In many embodiments, system 300 can comprise graphical user interface(“GUI”) 330, 331. In the same or different embodiments, GUI 330, 331 canbe part of and/or displayed by user computers 340, 341, which also canbe part of system 300. In some embodiments, GUI 330, 331 can comprisetext and/or graphics (image) based user interfaces. In the same ordifferent embodiments, GUI 330, 331 can comprise a heads up display(“HUD”). When GUI 330, 331 comprises a HUD, GUI 330, 331 can beprojected onto glass or plastic, displayed in midair as a hologram, ordisplayed on monitor 106 (FIG. 1). In various embodiments, GUI 330, 331can be color or black and white. In many embodiments, GUI 330, 331 cancomprise an application running on a computer system, such as computersystem 100, user computers 340, 341, and/or server computer 310. In thesame or different embodiments, GUI 330, 331 can comprise a websiteaccessed through internet 320. In some embodiments, GUI 330, 331 cancomprise an eCommerce website. In the same or different embodiments, GUI330, 331 can be displayed as or on a virtual reality (VR) and/oraugmented reality (AR) system or display.

In some embodiments, web server 320 can be in data communication throughInternet 330 with user computers (e.g., 340, 341). In certainembodiments, user computers 340-341 can be desktop computers, laptopcomputers, smart phones, tablet devices, and/or other endpoint devices.Web server 320 can host one or more websites. For example, web server320 can host an eCommerce web site that allows users to browse and/orsearch for products, to add products to an electronic shopping cart,and/or to purchase products, in addition to other suitable activities.

In many embodiments, server computer 310, internet 320, GUI 330, 331,and/or user computer 340, 341 can each comprise one or more inputdevices (e.g., one or more keyboards, one or more keypads, one or morepointing devices such as a computer mouse or computer mice, one or moretouchscreen displays, a microphone, etc.), and/or can each comprise oneor more display devices (e.g., one or more monitors, one or more touchscreen displays, projectors, etc.). In these or other embodiments, oneor more of the input device(s) can be similar or identical to keyboard104 (FIG. 1) and/or a mouse 110 (FIG. 1). Further, one or more of thedisplay device(s) can be similar or identical to monitor 106 (FIG. 1)and/or screen 108 (FIG. 1). The input device(s) and the displaydevice(s) can be coupled to the processing module(s) and/or the memorystorage module(s) server computer 310, internet 320, GUI 330, 331,and/or user computer 340, 341 in a wired manner and/or a wirelessmanner, and the coupling can be direct and/or indirect, as well aslocally and/or remotely. As an example of an indirect manner (which mayor may not also be a remote manner), a keyboard-video-mouse (KVM) switchcan be used to couple the input device(s) and the display device(s) tothe processing module(s) and/or the memory storage module(s). In someembodiments, the KVM switch also can be part of server computer 310,internet 320, GUI 330, 331, and/or user computer 340, 341. In a similarmanner, the processing module(s) and the memory storage module(s) can belocal and/or remote to each other.

In many embodiments, server computer 310, internet 320, GUI 330, 331,and/or user computer 340, 341 can be configured to communicate with oneor more user computers 340 and 341. In some embodiments, user computers340 and 341 also can be referred to as customer computers. In someembodiments, server computer 310, internet 320, GUI 330, 331, and/oruser computer 340, 341 can communicate or interface (e.g., interact)with one or more customer computers (such as user computers 340 and 341)through a network or internet 330. Internet 330 can be an intranet thatis not open to the public. Accordingly, in many embodiments, servercomputer 310, internet 320, GUI 330, 331, and/or user computer 340, 341(and/or the software used by such systems) can refer to a back end ofsystem 300 operated by an operator and/or administrator of system 300,and user computers 340 and 341 (and/or the software used by suchsystems) can refer to a front end of system 300 used by one or moreusers 350 and 351, respectively. In some embodiments, users 350 and 351also can be referred to as customers, in which case, user computers 340and 341 can be referred to as customer computers. In these or otherembodiments, the operator and/or administrator of system 300 can managesystem 300, the processing module(s) of system 300, and/or the memorystorage module(s) of system 300 using the input device(s) and/or displaydevice(s) of system 300.

Meanwhile, in many embodiments, server computer 310, internet 320, GUI330, 331, and/or user computer 340, 341 also can be configured tocommunicate with one or more databases. The one or more databases cancomprise a product database that contains information about products,items, or SKUs (stock keeping units) sold by a retailer. The one or moredatabases can be stored on one or more memory storage modules (e.g.,non-transitory memory storage module(s)), which can be similar oridentical to the one or more memory storage module(s) (e.g.,non-transitory memory storage module(s)) described above with respect tocomputer system 100 (FIG. 1). Also, in some embodiments, for anyparticular database of the one or more databases, that particulardatabase can be stored on a single memory storage module of the memorystorage module(s), and/or the non-transitory memory storage module(s)storing the one or more databases or the contents of that particulardatabase can be spread across multiple ones of the memory storagemodule(s) and/or non-transitory memory storage module(s) storing the oneor more databases, depending on the size of the particular databaseand/or the storage capacity of the memory storage module(s) and/ornon-transitory memory storage module(s).

The one or more databases can each comprise a structured (e.g., indexed)collection of data and can be managed by any suitable databasemanagement systems configured to define, create, query, organize,update, and manage database(s). Exemplary database management systemscan include MySQL (Structured Query Language) Database, PostgreSQLDatabase, Microsoft SQL Server Database, Oracle Database, SAP (Systems,Applications, & Products) Database, and IBM DB2 Database.

Meanwhile, communication between server computer 310, internet 320, GUI330, 331, and/or user computer 340, 341, and/or the one or moredatabases can be implemented using any suitable manner of wired and/orwireless communication. Accordingly, system 300 can comprise anysoftware and/or hardware components configured to implement the wiredand/or wireless communication. Further, the wired and/or wirelesscommunication can be implemented using any one or any combination ofwired and/or wireless communication network topologies (e.g., ring,line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols(e.g., personal area network (PAN) protocol(s), local area network (LAN)protocol(s), wide area network (WAN) protocol(s), cellular networkprotocol(s), powerline network protocol(s), etc.). Exemplary PANprotocol(s) can comprise Bluetooth, Zigbee, Wireless Universal SerialBus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) cancomprise Institute of Electrical and Electronic Engineers (IEEE) 802.3(also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; andexemplary wireless cellular network protocol(s) can comprise GlobalSystem for Mobile Communications (GSM), General Packet Radio Service(GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized(EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal MobileTelecommunications System (UMTS), Digital Enhanced CordlessTelecommunications (DECT), Digital AMPS (IS-136/Time Division MultipleAccess (TDMA)), Integrated Digital Enhanced Network (iDEN), EvolvedHigh-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc.The specific communication software and/or hardware implemented candepend on the network topologies and/or protocols implemented, and viceversa. In many embodiments, exemplary communication hardware cancomprise wired communication hardware including, for example, one ormore data buses, such as, for example, universal serial bus(es), one ormore networking cables, such as, for example, coaxial cable(s), opticalfiber cable(s), and/or twisted pair cable(s), any other suitable datacable, etc. Further exemplary communication hardware can comprisewireless communication hardware including, for example, one or moreradio transceivers, one or more infrared transceivers, etc. Additionalexemplary communication hardware can comprise one or more networkingcomponents (e.g., modulator-demodulator components, gateway components,etc.).

In many embodiments, the techniques described herein can provide severaltechnological improvements. Specifically, the techniques describedherein provide for the ability to determine an intent of a user over acomputer network based upon in-session interactions with a GUI. Thisapproach is different from previous approaches, which were unable todetermine intents of a user based upon in-session interactions with aGUI. Further, by customizing a GUI on a user computer in response tothis intent, navigation by the user on the user computer can be greatlyimproved and tailored to the user's specific intent. This can beespecially applicable when an intent is identified that would make itdifficult for a user to accomplish this intent on a computer with asmall screen, such as on a mobile electronic device as described above.Further, these small screens provide very little area for displayingpictures and/or text on a GUI. The techniques described herein can beused to more efficiently utilize this limited display area (e.g., byrearranging a GUI for easier navigation or displaying relevantinformation on a GUI pertaining to the user's state) in response to anidentified intent of a user.

Turning ahead in the drawings, FIG. 4 illustrates a flow chart for amethod 400, according to an embodiment. Method 400 is merely exemplaryand is not limited to the embodiments presented herein. Method 400 canbe employed in many different embodiments or examples not specificallydepicted or described herein. In some embodiments, the activities ofmethod 400 can be performed in the order presented. In otherembodiments, the activities of method 400 can be performed in anysuitable order. In still other embodiments, one or more of theactivities of method 400 can be combined or skipped. In manyembodiments, system 300 (FIG. 3) can be suitable to perform method 400and/or one or more of the activities of method 400. In these or otherembodiments, one or more of the activities of method 400 can beimplemented as one or more computer instructions configured to run atone or more processing modules and configured to be stored at one ormore non-transitory memory storage modules Such non-transitory memorystorage modules can be part of a computer system such as server computer310, internet 320, GUI 330, 331, and/or user computer 340, 341 (FIG. 3).The processing module(s) can be similar or identical to the processingmodule(s) described above with respect to computer system 100 (FIG. 1).

In many embodiments, method 400 can comprise an activity 401 ofgathering historical data. In various embodiments, historical data cancomprise interactions of a user with a first GUI, a past geographicallocation of a user, and/or demographics of a user. In some embodiments,a first GUI can comprise GUI 330, 331 (FIG. 3), a second GUI asdescribed in activity 402 (FIG. 4), a third GUI as described in activity408 (FIG. 4), a fourth GUI as described in activity 503 (FIG. 5), and/ora fifth GUI as described in activity 506 (FIG. 5). In the same ordifferent embodiments, interactions of a user with a first GUI cancomprise views of an item of a category of items, cart adds of an itemof a category of items, registry adds of an item of a category of items,transactions involving an item of the category of items, searches forthe item of the category of items, mouse movements of a user, touch padmovements of a user, touchscreen interactions of a user, and/or eyemovements of a user. In many embodiments, historical data can becollected at a first time. In some embodiments, a first time can bedescribed relative to a second time as described in activity 403. Invarious embodiments, a first time can be earlier than a second time. Inthe same or different embodiments, a first time can comprise a timeperiod, such as 1 day, 2 days, 3 days, 4 days, 5 days, 1 month, 2months, 3 months, 4 months, 5 months, 1 year, 2 years, 3 years, 4 years,5 years, etc. In some embodiments, activity 401 and other activities inmethod 400 can comprise using a distributed network comprisingdistributed memory architecture to gather historical data. Thisdistributed architecture can reduce the impact on the network and systemresources to reduce congestion in bottlenecks while still allowing datato be accessible from a central location. Using distributed architecturecan be especially applicable for gathering historic data becausegathering large datasets can reduce processing speeds and increaseprocessing burdens on single processor computer systems as well asincrease storage burdens on non-distributed systems.

In many embodiments, after activity 401, method 400 can continue with orcomprise an activity 402 of storing historical data as at least onehistorical feature vector. In various embodiments, a feature vector canbe configured to be used in a machine learning algorithm, as describedin activities 405-408 and/or activity 505. In the same or differentembodiments, historical data can be stored over discrete time periods.In many embodiments, a discrete time period can comprise a time period,such as 1 day, 2 days, 3 days, 4 days, 5 days, 1 month, 2 months, 3months, 4 months, 5 months, 1 year, 2 years, 3 years, 4 years, 5 years,etc. In the same or different embodiments, a historical feature vectorcan comprise interactions of a user with a first GUI, a pastgeographical location of a user, and/or demographics of a user. In someembodiments, a first GUI can comprise GUI 330, 331 (FIG. 3), a secondGUI as described in activity 402 (FIG. 4), a third GUI as described inactivity 408 (FIG. 4), a fourth GUI as described in activity 503 (FIG.5), and/or a fifth GUI as described in activity 506 (FIG. 5). In variousembodiments, when interactions of a user with a first GUI occur, a countcan be added to a historical feature vector for that interaction. Forexample, when a user interacts with a website for an item comprising ataxonomy of “Electronics/Camera/SLRcameras/Canon” counts will be addedto historical feature vectors for: “Electronics,” “Electronics/Camera,”“Electronics/Camera/SLRcameras,” and“Electronics/Camera/SLRcameras/Canon.” In many embodiments, a historicalfeature vector can comprise information about a static attribute of auser. For example, a static attribute can comprise demographicinformation (e.g. gender, race, etc.). In embodiments where a historicalfeature vector comprises information about a static attribute of a user,a count can be assigned to a specific value of the static attribute. Forexample, when a gender of a user comprises male, a count of 20 can beapplied to a historical feature vector for gender, and, when a gender ofa user comprises female, a count of 25 can be applied to a historicalfeature vector for gender. In some embodiments, activity 402 and otheractivities in method 400 can comprise using a distributed networkcomprising distributed memory architecture to store historical data.This distributed architecture can reduce the impact on the network andsystem resources to reduce congestion in bottlenecks while stillallowing data to be accessible from a central location. Usingdistributed architecture can be especially applicable for storinghistoric data, as storing large datasets can reduce storage capacitythereby slowing down non-distributed systems.

In many embodiments, after activity 402, method 400 can continue with orcomprise activity 403 of gathering in-session data. In variousembodiments, in-session data can comprise interactions of a user with asecond GUI, a current geographical location of a user, a date on whichin-session data is gathered, weather patterns at the geographicallocation of the user, current events at the geographical location of theuser, and/or demographics of a user. In some embodiments, a second GUIcan comprise GUI 330, 331 (FIG. 3), a first GUI as described in activity401, a third GUI as described in activity 408, a fourth GUI as describedin activity 503 (FIG. 5), and/or a fifth GUI as described in activity506 (FIG. 5). In the same or different embodiments, interactions of auser with a second GUI can comprise views of an item of a category ofitems, cart adds of an item of a category of items, registry adds of anitem of a category of items, transactions involving an item of thecategory of items, searches for the item of the category of items, mousemovements of a user, touch pad movements of a user, touchscreeninteractions of a user, and/or eye movements of a user. In manyembodiments, in-session data can be collected at a second time. In someembodiments, a second time can be described relative to a first time asdescribed in activity 401. In various embodiments, a second time can belater than a first time. In the same or different embodiments, a secondtime can comprise a time period such as 1 second, 2 seconds, 3 seconds,4 seconds, 5 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5minutes, 1 hours, 2 hours, 3 hours, 4 hours, 5 hours, 1 day, 2 days, 3days, 4 days, 5 days, 1 month, 2 months, 3 months, 4 months, 5 months, 1year, 2 years, 3 years, 4 years, 5 years, etc. In various embodiments,interactions of a user with a second GUI can occur within only one usersession. In the same or different embodiments, a user session can beginwhen a user logs into an account and can end when a user logs out of theaccount. In various embodiments, a user session can begin when a usernavigates to a webpage, and can end when a user navigates away from awebpage. In the same or different embodiments, a user session can beginwhen a user navigates to a website, and can end when a user navigatesaway from a website and/or completes a task on a website. In someembodiments, activity 403 and other activities in method 400 cancomprise using a distributed network comprising distributed memoryarchitecture to gather in-session data. This distributed architecturecan reduce the impact on the network and system resources to reducecongestion in bottlenecks while still allowing data to be accessiblefrom a central location.

In many embodiments, after activity 403, method 400 can continue with orcomprise an activity 404 of storing in-session data as at least onefeature vector. In various embodiments, a feature vector can beconfigured to be used in a machine learning algorithm, as described inactivities 405-408 and/or activity 505. In various embodiments, wheninteractions of a user with a second GUI occur, a count can be added toa feature vector for that interaction. For example, when a userinteracts with a website for an item comprising a taxonomy of“Electronics/Camera/SLRcameras/Canon” counts will be added to featurevectors for: “Electronics,” “Electronics/Camera,”“Electronics/Camera/SLRcameras,” and“Electronics/Camera/SLRcameras/Canon.” In many embodiments, a featurevector can comprise information about a static attribute of a user. Forexample, a static attribute can comprise demographic information (e.g.gender, race, etc.). In embodiments where a feature vector comprisesinformation about a static attribute of a user, a count can be assignedto a specific value of the static attribute. For example, when a genderof a user comprises male, a count of 20 can be applied to a featurevector for gender, and, when a gender of a user comprises female, acount of 25 can be applied to a feature vector for gender. In someembodiments, activity 404 and other activities in method 400 cancomprise using a distributed network comprising distributed memoryarchitecture to store in-session data. This distributed architecture canreduce the impact on the network and system resources to reducecongestion in bottlenecks while still allowing data to be accessiblefrom a central location.

In many embodiments, after activity 404, method 400 can continue with orcomprise an activity 405 of training a multi-class classifier. In thesame or different embodiments, training a multi-class classifier can bereferred to as using a machine learning algorithm. In some embodiments,training a multi-class classifier can comprise estimating internalparameters of a model configured to classify an intent of a user from aset of different intents. In various embodiments, a multi-classclassifier can be trained using labeled training data otherwise known asa training dataset. In many embodiments, a training dataset can compriseall or a part of historical data, as described in activities 401-402and/or activities 501-502 (FIG. 5), that has been labeled with anintent. In some embodiments, a training dataset can be defined asD={x_(t), c_(t)}_(t-0) ^(T), wherein each x_(t) comprises a t^(th)feature vector (u_(h), u_(s), i, a)^(t) and c_(t) comprises acorresponding label. In various embodiments, a multi-class classifiercan be trained using a logistic regression function by maximizing a loglikelihood of data D. In many embodiments, a multi-class classifier canbe configured to determine a probability of a user having one or moreintents.

In the same or different embodiments, a multi-class classifier can betrained on a feed-forward neural network. As compared to a recurrentneural network, a feed forward neural network can comprise a neuralnetwork where connections between nodes in the network no not form acycle. In other words, information is passed through a feed forwardneural network in one direction: from input nodes, to hidden nodes whena hidden layer is used, and then to output nodes. In more specificembodiments, a feed forward neural network can comprise a neural networkhaving two hidden layers. In some embodiments, each hidden layer cancomprise 500 nodes.

In many embodiments, a node of a neural network can have an activationfunction also known as a rectifier. In the same or differentembodiments, a rectifier can comprise a rectified linear unit (“ReLU”).In various embodiments, a node of a neural network can have a ReLU witha non-linear output, otherwise known as ReLU non-linearity. In manyembodiments, a rectifier can comprise an equation comprising:

${{\arg \max}_{w}{\sum\limits_{t}{\log \; {p\left( {\left. c_{t} \middle| x_{t} \right.,w} \right)}}}} = {{\arg \max}_{w}{\sum\limits_{t}{\frac{1}{1 + {\exp \left( {{- w^{t}}x_{t}} \right)}}.}}}$

In the same or different embodiments, each x_(t) comprises a t^(th)feature vector, c_(t) comprises a corresponding label, and/or w cancomprise a feature weight vector.

ReLU non-linearity can provide many advantages over traditional ReLUs.For example, ReLU non-linearity can allow for training of deepsupervised neural networks without unsupervised pre-training. Thistraining, though, can increase training time for multi-class classifierson feed forward neural networks. Therefore, in some embodiments,activity 405 and other activities of method 400 can comprise using adistributed network comprising distributed memory architecture to traina multi-class classifier. This distributed architecture can reduce theimpact on the network and system resources to reduce congestion inbottlenecks while still allowing data to be accessible from a centrallocation. Use of distributed networks for training a multi-classclassifier are especially applicable when ReLU non-linearity is used, ascomputation of large datasets can reduce processing speeds and increaseprocessing burdens on single processor computer systems. In someembodiments, activity 405 and other activities in method 400 cancomprise using a distributed network comprising distributed memoryarchitecture to train a multi-class classifier. This distributedarchitecture can reduce the impact on the network and system resourcesto reduce congestion in bottlenecks while still allowing data to beaccessible from a central location. Use of distributed networks areespecially applicable for training a multi-class classifier, ascomputation of large datasets can reduce processing speeds and increaseprocessing burdens on single processor computer systems.

In many embodiments, after activity 404, method 400 can continue with orcomprise an activity 406 of training a binary classifier. In the same ordifferent embodiments, training a binary classifier can be referred toas using a machine learning algorithm. In some embodiments, training abinary classifier can comprise estimating internal parameters of a modelconfigured to determine a probability of a user having an intent. Invarious embodiments, a binary classifier can be trained using labeledtraining data otherwise known as a training dataset. In manyembodiments, a training dataset can comprise all or a part of historicaldata, as described in activities 401-402 and/or activities 501-502 (FIG.5), that has been labeled with an intent. In some embodiments, atraining dataset can be defined as D={x_(t), c_(t)}_(t-0) ^(T), whereineach x_(t) comprises a t^(th) feature vector (u_(h), u_(s), i, a)^(t)and c_(t) comprises a corresponding label. In various embodiments, abinary classifier can be trained using a logistic regression function bymaximizing a log likelihood of data D. In various embodiments, a binaryclassifier can be configured to determine a probability of a user havingonly one intent. In the same or different embodiments, a binaryclassifier can be trained on a feed-forward neural network. As comparedto a recurrent neural network, a feed forward neural network cancomprise a neural network where connections between nodes in the networkno not form a cycle. In other words, information is passed through afeed forward neural network in one direction: from the input nodes, tothe hidden nodes when a hidden layer is used, and then to the outputnodes. In more specific embodiments, a feed forward neural network cancomprise a neural network having two hidden layers. In some embodiments,each hidden layer can comprise 500 nodes.

In many embodiments, a node of a neural network can have an activationfunction also known as a rectifier. In the same or differentembodiments, a rectifier can comprise a rectified linear unit (“ReLU”).In various embodiments, a node of a neural network can have a ReLU witha non-linear output, otherwise known as ReLU non-linearity. In manyembodiments, a rectifier can comprise an equation comprising:

${{\arg \max}_{w}{\sum\limits_{t}{\log \; {p\left( {\left. c_{t} \middle| x_{t} \right.,w} \right)}}}} = {{\arg \max}_{w}{\sum\limits_{t}{\frac{1}{1 + {\exp \left( {{- w^{t}}x_{t}} \right)}}.}}}$

In the same or different embodiments, t can comprise a number of featurevectors as time progresses, c_(t) can comprise a number of interactionswith a GUI, x_(t) can comprise a feature vector, and/or w can comprise afeature weight vector.

ReLU non-linearity can provide many advantages over traditional ReLUs.For example, ReLU non-linearity can allow for training of deepsupervised neural networks without unsupervised pre-training. Thistraining, though, can increase training time for multi-class classifierson feed forward neural networks. Therefore, in some embodiments,activity 406 and other activities of method 400 can comprise using adistributed network comprising distributed memory architecture to traina binary classifier. This distributed architecture can reduce the impacton the network and system resources to reduce congestion in bottleneckswhile still allowing data to be accessible from a central location. Useof distributed networks for training a binary classifier are especiallyapplicable when ReLU non-linearity is used, as computation of largedatasets can reduce processing speeds and increase processing burdens onsingle processor computer systems. In some embodiments, activity 406 andother activities in method 400 can comprise using a distributed networkcomprising distributed memory architecture to train a binary classifier.This distributed architecture can reduce the impact on the network andsystem resources to reduce congestion in bottlenecks while stillallowing data to be accessible from a central location. Use ofdistributed networks are especially applicable for training a binaryclassifier, as computation of large datasets can reduce processingspeeds and increase processing burdens on single processor computersystems.

In many embodiments, after any of activities 404, 405, or 406, method400 can continue with or comprise an activity 407 of determining anintent of a user. In some embodiments, an intent of a user can comprisea predicted interaction of a user with a GUI and/or a predictedshort-term goal of the user with regards to a GUI. For example, anintent can comprise a user interaction with a certain aspect of a GUI(e.g. clicking or selecting a specific element of the GUI, looking at aspecific potion of a GUI, etc.) and/or completing a specific task usinga GUI (e.g. purchasing an item, selecting in-store pick-up, navigatingto a specific webpage using the GUI, etc.). In the same or differentembodiments, an intent of a user can be determined using at least onehistorical feature vector as described in activity 402. In variousembodiments, an intent of a user can be determined using at least onein-session feature vector, as described in activity 404. In someembodiments, an intent of a user can be determined using a multi-classclassifier, as described in activity 405. In the same or differentembodiments, using a multi-class classifier can be referred to as usinga machine learning algorithm. In many embodiments, an intent of a usercan be determined using a binary classifier, as described in activity406. In various embodiments, using a binary classifier can be referredto as using a machine learning algorithm. In the same or differentembodiments, determining an intent of a user can comprise determining aprobability of a user having an intent. In various embodiments, when aprobability of a user having an intent is above a predefined threshold,activity 408 can be performed. In the same or different embodiments,determining an intent of a user can comprise using a set of equationscomprising:

${{{{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)} = {\sum\limits_{i \in }{P\left( {c,\left. i \middle| u_{h} \right.,u_{s},a} \right)}}};} = {\sum\limits_{i \in }{{P\left( {\left. i \middle| u_{h} \right.,u_{s},a} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)}}}};{{and} = {\sum\limits_{i \in }{{P\left( i \middle| u_{s} \right)}{{P\left( {\left. c \middle| u_{h} \right.,u_{s},i,a} \right)}.}}}}$

In the same or different embodiments, c comprises a probability of auser having a specific intent, u_(h) comprises at least one historicalfeature vector, u_(s) comprises at least one in-session feature vector,i comprises a user intent, J can comprise an intent of a set of intents,and/or a can comprise an asset of a set of assets. In many embodiments,an asset can comprise an element of a GUI that altered according to adisclosed system and/or method.

In some embodiments, activity 407 and other activities in method 400 cancomprise using a distributed network comprising distributed memoryarchitecture to determine an intent of a user. This distributedarchitecture can reduce the impact on the network and system resourcesto reduce congestion in bottlenecks while still allowing data to beaccessible from a central location. Use of distributed networks areespecially applicable for determining an intent of a user, ascomputation of large datasets can reduce processing speeds and increaseprocessing burdens on single processor computer systems.

In many embodiments, after activity 407, method 400 can continue with orcomprise activity 408 of transmitting instructions to display a GUI. Inmany embodiments, a GUI transmitted for display during activity 408 canbe referred to as a third GUI. In some embodiments, transmittinginstructions to display a GUI can occur in response to activity 407 ofdetermining an intent of a user. In some embodiments, a first GUI cancomprise GUI 330, 331 (FIG. 3), a first GUI as described in activity401, a second GUI as described in activity 402, a fourth GUI asdescribed in activity 503 (FIG. 5), and/or a fifth GUI as described inactivity 506 (FIG. 5). In the same or different embodiments, a GUItransmitted for display during activity 408 can be similar or differentthan a GUI as described in activities 401, 402, 503 (FIG. 5), and/or 506(FIG. 5). In various embodiments, transmitting instructions to display aGUI can comprise customizing a content of a GUI. In the same ordifferent embodiments, customizing a content on a GUI can comprisealtering an image displayed on the GUI, altering text on the GUI,altering a layout of the GUI, changing a type of the GUI, displaying anadvertisement on the GUI, displaying no advertisement on the GUI,altering a color displayed on the GUI, etc. In many embodiments,customizing a GUI can comprise displaying certain content at specifictimes on the GUI. In further embodiments, content displayed on a GUI cancomprise advertisements for products, services, and/or events. Invarious embodiments, a GUI transmitted for display during activity 408can be related to an intent of a user. In many embodiments, a GUItransmitted for display during activity 408 can be optimized in order tofacilitate the intent of the user. In many embodiments, transmitting aGUI for display during activity 408 can occur when a user is in a samesession as described in activities 403-404. In some embodiments,activity 408 and other activities in method 400 can comprise using adistributed network comprising distributed memory architecture totransmit instructions to display a GUI. This distributed architecturecan reduce the impact on the network and system resources to reducecongestion in bottlenecks while still allowing data to be accessiblefrom a central location.

Turning ahead in the drawings, FIG. 5 illustrates a flow chart for amethod 500, according to an embodiment. Method 500 is merely exemplaryand is not limited to the embodiments presented herein. Method 500 canbe employed in many different embodiments or examples not specificallydepicted or described herein. In some embodiments, the activities ofmethod 500 can be performed in the order presented. In otherembodiments, the activities of method 500 can be performed in anysuitable order. In still other embodiments, one or more of theactivities of method 500 can be combined or skipped. In variousembodiments, method 500 can be performed as a part of, in conjunctionwith, and/or completely separate from method 400 (FIG. 4). In manyembodiments, system 300 (FIG. 3) can be suitable to perform method 500and/or one or more of the activities of method 500. In these or otherembodiments, one or more of the activities of method 500 can beimplemented as one or more computer instructions configured to run atone or more processing modules and configured to be stored at one ormore non-transitory memory storage modules, such non-transitory memorystorage modules can be part of a computer system such as server computer310, internet 320, GUI 330, 331, and/or user computer 340, 341 (FIG. 3).The processing module(s) can be similar or identical to the processingmodule(s) described above with respect to computer system 100 (FIG. 1).

In many embodiments, method 500 can comprise an activity 501 ofcombining historical data and in-session data. In many embodiments, acombination of historical data and in-session data can be referred to assecond historical data. In the same or different embodiments, thehistorical data of activity 501 can comprise historical data asdescribed in method 400 (FIG. 4). In various embodiments, the in-sessiondata of activity 501 can comprise in-session data as described in method400 (FIG. 4). In some embodiments, activity 501 can occur after a usersession has ended, as described in activities 403-404. In the same ordifferent embodiments, second historical data can be labeled used totrain a multi-class classifier and/or a binary classifier as describedin activity 405 (FIG. 4) or activity 406 (FIG. 4), respectively. In someembodiments, activity 501 and other activities in method 500 cancomprise using a distributed network comprising distributed memoryarchitecture to combine historical data and in-session data. Thisdistributed architecture can reduce the impact on the network and systemresources to reduce congestion in bottlenecks while still allowing datato be accessible from a central location. Using distributed architecturecan be especially applicable for combining historical data andin-session data, as combining large datasets can reduce processingspeeds and increase processing burdens on single processor computersystems.

In many embodiments, method 500 can continue with or comprise anactivity 502 of storing second historical data as at least one secondhistorical feature vector. In various embodiments, a second historicalfeature vector can be configured to be used in a machine learningalgorithm, as described in activities 405-407 and/or activity 505. Invarious embodiments, when interactions of a user with a GUI occur, acount can be added to a second historical feature vector for thatinteraction. For example, when a user interacts with a website for anitem comprising a taxonomy of “Electronics/Camera/SLRcameras/Canon”counts will be added to historical feature vectors for: “Electronics,”“Electronics/Camera,” “Electronics/Camera/SLRcameras,” and“Electronics/Camera/SLRcameras/Canon.” In many embodiments, a secondhistorical feature vector can comprise information about a staticattribute of a user. For example, a static attribute can comprisedemographic information (e.g. gender, race, etc.). In embodiments wherea feature vector comprises information about a static attribute of auser, a count can be assigned to a specific value of the staticattribute. For example, when a gender of a user comprises male, a countof 20 can be applied to a second historical feature vector for gender,and, when a gender of a user comprises female, a count of 25 can beapplied to a second historical feature vector for gender.

In some embodiments, activity 502 and other activities in method 500 cancomprise using a distributed network comprising distributed memoryarchitecture to store historical data. This distributed architecture canreduce the impact on the network and system resources to reducecongestion in bottlenecks while still allowing data to be accessiblefrom a central location. Using distributed architecture can beespecially applicable for storing historic data, as storing largedatasets can reduce storage capacity thereby slowing downnon-distributed systems.

In many embodiments, method 500 can continue with or comprise anactivity 503 of gathering second in-session data. In some embodiments,second in-session data can be similar to in-session data as described inactivity 403. Therefore, in various embodiments, second in-session datacan comprise interactions of a user with a fourth GUI, a currentgeographical location of a user, a date on which in-session data isgathered, weather patterns at the geographical location of the user,current events at the geographical location of the user, and/ordemographics of a user. In some embodiments, a fourth GUI can compriseGUI 330, 331 (FIG. 3), a first GUI as described in activity 401 (FIG.4), a second GUI as described in activity 402 (FIG. 4), a third GUI asdescribed in activity 408 (FIG. 4), and/or a fifth GUI as described inactivity 506 (FIG. 5). In the same or different embodiments,interactions of a user with a fourth GUI can comprise views of an itemof a category of items, cart adds of an item of a category of items,registry adds of an item of a category of items, transactions involvingan item of the category of items, searches for the item of the categoryof items, mouse movements of a user, touch pad movements of a user,touchscreen interactions of a user, and/or eye movements of a user. Inmany embodiments, in-session data can be collected at a third time. Insome embodiments, a third time can be described relative to a first timeand/or a second time, as described in activities 401 and/or 403. Invarious embodiments, a third time can be later than a first time and/ora second time. In the same or different embodiments, a third time cancomprise a time period such as 1 second, 2 seconds, 3 seconds, 4seconds, 5 seconds, 1 minute, 2 minutes, 3 minutes, 4 minutes, 5minutes, 1 hours, 2 hours, 3 hours, 4 hours, 5 hours, 1 day, 2 days, 3days, 4 days, 5 days, 1 month, 2 months, 3 months, 4 months, 5 months, 1year, 2 years, 3 years, 4 years, 5 years, etc. In various embodiments,interactions of a user with a fourth GUI can occur within only one usersession. In the same or different embodiments, a user session can beginwhen a user logs into an account and can end when a user logs out of theaccount. In various embodiments, a user session can begin when a usernavigates to a webpage, and can end when a user navigates away from thewebpage. In the same or different embodiments, a user session can beginwhen a user navigates to a website or completes a first task on thewebsite, and can end when a user navigates away from the website and/orcompletes a second task on the website. In many embodiments, a usersession occurring during activity 503 can be the same or different thana user session occurring in activity 403. In some embodiments, activity503 and other activities in method 500 can comprise using a distributednetwork comprising distributed memory architecture to gather in-sessiondata. This distributed architecture can reduce the impact on the networkand system resources to reduce congestion in bottlenecks while stillallowing data to be accessible from a central location.

In many embodiments, method 500 can continue with or comprise anactivity 504 of storing second in-session data as at least one secondin-session feature vector. In various embodiments, a second in-sessionfeature vector can be configured to be used in a machine learningalgorithm, as described in activities 405-407 and/or activity 505. Invarious embodiments, when interactions of a user with a fourth GUIoccur, a count can be added to a feature vector for that interaction.For example, when a user interacts with a website for an item comprisinga taxonomy of “Electronics/Camera/SLRcameras/Canon” counts will be addedto feature vectors for: “Electronics,” “Electronics/Camera,”“Electronics/Camera/SLRcameras,” and“Electronics/Camera/SLRcameras/Canon.” In many embodiments, a featurevector can comprise information about a static attribute of a user. Forexample, a static attribute can comprise demographic information (e.g.gender, race, etc.). In embodiments where a feature vector comprisesinformation about a static attribute of a user, a count can be assignedto a specific value of the static attribute. For example, when a genderof a user comprises male, a count of 20 can be applied to a featurevector for gender, and, when a gender of a user comprises female, acount of 25 can be applied to a feature vector for gender. In someembodiments, activity 504 and other activities in method 500 cancomprise using a distributed network comprising distributed memoryarchitecture to store in-session data. This distributed architecture canreduce the impact on the network and system resources to reducecongestion in bottlenecks while still allowing data to be accessiblefrom a central location.

In many embodiments, method 500 can continue with or comprise anactivity 505 of determining an intent of a user. In some embodiments, anintent of a user can comprise a predicted interaction of a user with aGUI and/or a predicted short-term goal of the user with regards to aGUI. For example, an intent can comprise a user interaction with acertain aspect of a GUI (e.g. clicking or selecting a specific elementof the GUI, looking at a specific potion of a GUI, etc.) and/orcompleting a specific task using a GUI (e.g. purchasing an item,selecting in-store pick-up, navigating to a specific webpage using theGUI, etc.). In the same or different embodiments, an intent of a usercan be determined using at least one second historical feature vector,as described in activity 502. In various embodiments, an intent of auser can be determined using at least one second in-session featurevector, as described in activity 504. In some embodiments, an intent ofa user can be determined using a multi-class classifier, as described inactivity 405. In the same or different embodiments, using a multi-classclassifier can be referred to as using a machine learning algorithm. Inmany embodiments, an intent of a user can be determined using a binaryclassifier, as described in activity 406. In various embodiments, usinga binary classifier can be referred to as using a machine learningalgorithm. In the same or different embodiments, determining an intentof a user can comprise determining a probability of a user having anintent. In various embodiments, when a probability of a user having anintent is above a predefined threshold, activity 506 can be performed.In the same or different embodiments, determining an intent of a usercan comprise using a set of equations comprising:

${{{{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)} = {\sum\limits_{i \in }{P\left( {c,\left. i \middle| u_{h} \right.,u_{s},a} \right)}}};} = {\sum\limits_{i \in }{{P\left( {\left. i \middle| u_{h} \right.,u_{s},a} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)}}}};{{and} = {\sum\limits_{i \in }{{P\left( i \middle| u_{s} \right)}{{P\left( {\left. c \middle| u_{h} \right.,u_{s},i,a} \right)}.}}}}$

In the same or different embodiments, c comprises a probability of auser having a specific intent, u_(h) comprises at least one historicalfeature vector, u_(s) comprises at least one in-session feature vector,i comprises a user intent, J can comprise an intent of a set of intents,and/or a can comprise an asset of a set of assets. In many embodiments,an asset can comprise an element of a GUI that altered according to adisclosed system and/or method.

In some embodiments, activity 505 and other activities in method 500 cancomprise using a distributed network comprising distributed memoryarchitecture to determine an intent of a use. This distributedarchitecture can reduce the impact on the network and system resourcesto reduce congestion in bottlenecks while still allowing data to beaccessible from a central location. Use of distributed networks areespecially applicable for determining an intent of a user, ascomputation of large datasets can reduce processing speeds and increaseprocessing burdens on single processor computer systems.

In many embodiments, method 500 can continue with or comprise activity506 of transmitting instructions to display a GUI. In some embodiments,transmitting instructions to display a GUI can occur in response toactivity 505 of determining an intent of a user. In various embodiments,a GUI transmitted for display in activity 506 can be referred to as afifth GUI. In some embodiments, a fifth GUI can comprise GUI 330, 331(FIG. 3), a first GUI as described in activity 401 (FIG. 4), a secondGUI as described in activity 402 (FIG. 4), a third GUI as described inactivity 408 (FIG. 4), and/or a fourth GUI as described in activity 503.In the same or different embodiments, a GUI transmitted for displayduring activity 506 can be similar or different than a GUI as describedin activities 401 (FIG. 4), 403 (FIG. 4), 408 (FIG. 4), and/or 503. Invarious embodiments, transmitting instructions to display a GUI cancomprise customizing a content of a GUI. In the same or differentembodiments, customizing a content on a GUI can comprise altering animage displayed on the GUI, altering text on the GUI, altering a layoutof the GUI, changing a type of the GUI, displaying an advertisement onthe GUI, displaying no advertisement on the GUI, altering a colordisplayed on the GUI, etc. In many embodiments, customizing a content ona GUI can comprise displaying certain content at specific times. Infurther embodiments, a content on a GUI can comprise advertisements forproducts, services, and/or events. In various embodiments, a GUItransmitted for display during activity 408 can be related to an intentof a user. In many embodiments, a GUI transmitted for display duringactivity 506 can be optimized in order to facilitate the intent of auser. In many embodiments, transmitting a GUI display during activity506 can occur when a user is in the same session as described inactivities 403-404 and/or 503-504. In some embodiments, activity 506 andother activities in method 500 can comprise using a distributed networkcomprising distributed memory architecture to transmit instructions todisplay a GUI. This distributed architecture can reduce the impact onthe network and system resources to reduce congestion in bottleneckswhile still allowing data to be accessible from a central location.

Turning ahead in the drawings, FIG. 6 illustrates a block diagram of asystem 600 that can be employed for altering a user interface usingpredicted user activity. System 600 is merely exemplary and embodimentsof the system are not limited to the embodiments presented herein. Insome embodiments, system 600 can be the same as system 300 (FIG. 3)System 600 can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments, certainelements or modules of system 600 can perform various procedures,processes, and/or activities. In these or other embodiments, theprocedures, processes, and/or activities can be performed by othersuitable elements or modules of system 600.

Generally, therefore, system 600 can be implemented with hardware and/orsoftware, as described herein. In some embodiments, part or all of thehardware and/or software can be conventional, while in these or otherembodiments, part or all of the hardware and/or software can becustomized (e.g., optimized) for implementing part or all of thefunctionality of system 600 described herein.

In many embodiments, system 600 can comprise non-transitory memorystorage module 601. Memory storage module 601 can be referred to ashistorical data gathering module 601. In many embodiments, historicaldata gathering module 601 can store computing instructions configured torun on one or more processing modules and perform one or more acts ofmethod 400 (FIG. 4) (e.g., activity 401 (FIG. 4)) and/or one or moreacts of method 500 (FIG. 5) (e.g., activity 501 (FIG. 5)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 602. Memory storage module 602 can be referred to ashistorical data storing module 602. In many embodiments, historical datastoring module 602 can store computing instructions configured to run onone or more processing modules and perform one or more acts of method400 (FIG. 4) (e.g., activity 402 (FIG. 4)) and/or one or more acts ofmethod 500 (FIG. 5) (e.g., activity 502 (FIG. 5)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 603. Memory storage module 603 can be referred to asin-session data gathering module 603. In many embodiments, in-sessiondata gathering module 603 can store computing instructions configured torun on one or more processing modules and perform one or more acts ofmethod 400 (FIG. 4) (e.g., activity 403 (FIG. 4)) and/or one or moreacts of method 500 (FIG. 5) (e.g., activity 504 (FIG. 5)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 604. Memory storage module 604 can be referred to asin-session data storing module 604. In many embodiments, in-session datastoring module 604 can store computing instructions configured to run onone or more processing modules and perform one or more acts of method400 (FIG. 4) (e.g., activity 404 (FIG. 4)) and/or one or more acts ofmethod 500 (e.g., activity 504 (FIG. 5)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 605. Memory storage module 605 can be referred to asmulti-class classifier training module 605. In many embodiments,multi-class classifier training module 605 can store computinginstructions configured to run on one or more processing modules andperform one or more acts of method 400 (FIG. 4) (e.g., activity 405(FIG. 4)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 606. Memory storage module 606 can be referred to asbinary classifier training module 606. In many embodiments, binaryclassifier training module 606 can store computing instructionsconfigured to run on one or more processing modules and perform one ormore acts of method 400 (FIG. 4) (e.g., activity 406 (FIG. 4)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 607. Memory storage module 607 can be referred to asintent determining module 607. In many embodiments, intent determiningmodule 607 can store computing instructions configured to run on one ormore processing modules and perform one or more acts of method 400 (FIG.4) (e.g., activity 407 (FIG. 4)) and/or one or more acts of method 500(FIG. 5) (e.g., 505 (FIG. 5)).

In many embodiments, system 600 can comprise non-transitory memorystorage module 608. Memory storage module 608 can be referred to as GUItransmitting module 608. In many embodiments, GUI transmitting module608 can store computing instructions configured to run on one or moreprocessing modules and perform one or more acts of method 400 (FIG. 4)(e.g., activity 408 (FIG. 4)) and/or one or more acts of method 500(FIG. 5) (e.g., activity 506 (FIG. 5)).

Although systems and methods for altering user interfaces usingpredicted user activity have been described with reference to specificembodiments, it will be understood by those skilled in the art thatvarious changes may be made without departing from the spirit or scopeof the disclosure. Accordingly, the disclosure of embodiments isintended to be illustrative of the scope of the disclosure and is notintended to be limiting. It is intended that the scope of the disclosureshall be limited only to the extent required by the appended claims. Forexample, to one of ordinary skill in the art, it will be readilyapparent that any element of FIGS. 1-6 may be modified, and that theforegoing discussion of certain of these embodiments does notnecessarily represent a complete description of all possibleembodiments. For example, one or more of the procedures, processes, oractivities of FIG. 4 may include different procedures, processes, and/oractivities and be performed by many different modules, in many differentorders.

All elements claimed in any particular claim are essential to theembodiment claimed in that particular claim. Consequently, replacementof one or more claimed elements constitutes reconstruction and notrepair. Additionally, benefits, other advantages, and solutions toproblems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat may cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A system comprising: one or more processors; andone or more non-transitory computer-readable storage media storingcomputing instructions configured to run on the one or more processorsand perform acts of: gathering historical data comprising interactionsof a user with a first graphical user interface at a first time; storingthe historical data comprising the interactions of the user with thefirst graphical user interface as at least one historical featurevector; gathering in-session data comprising interactions of the userwith a second graphical user interface at a second time later than thefirst time; storing the in-session data comprising the interactions ofthe user with the second graphical user interface as at least onein-session feature vector; determining an intent of the user using theat least one historical feature vector and the at least one in-sessionfeature vector; and transmitting instructions to display a thirdgraphical user interface for the user based upon the intent of the user.2. The system of claim 1, wherein the in-session data further comprises:a geographical location of the user; a date on which the in-session datais gathered; weather patterns at the geographical location of the user;current events at the geographical location of the user; or demographicsof the user.
 3. The system of claim 1, wherein the interactions of theuser with the second graphical user interface at the second time laterthan the first time occurs within only one user session.
 4. The systemof claim 3, wherein the computing instructions are further configured torun on the one or more processors and perform acts of: combining thehistorical data and the in-session data into second historical data;storing the second historical data as at least one second historicalfeature vector; gathering second in-session data comprising interactionsof the user with the third graphical user interface at a third timelater than the first time and the second time; storing the secondin-session data comprising the interactions of the user with the thirdgraphical user interface as at least one second in-session featurevector; determining a second intent of the user using the at least onesecond historical feature vector and the at least one second in-sessionfeature vector; and transmitting instructions to display a fourthgraphical user interface for the user based upon the second intent ofthe user.
 5. The system of claim 3, wherein: the only one user sessionbegins when the user logs into an account; and the only one user sessionends when the user logs out of the account.
 6. The system of claim 1,wherein: the computing instructions are further configured to run on theone or more processors and perform acts of: training a binary classifieron a feed forward neural network to identify the intent of the user; anddetermining the intent of the user using the at least one historicalfeature vector and the at least one in-session feature vector comprises:determining the intent of the user using the at least one historicalfeature vector, the at least one in-session feature vector, and thebinary classifier.
 7. The system of claim 6, wherein an output of eachnode in the feed forward neural network comprises a rectifier havingReLU non-linearity.
 8. The system of claim 7, wherein the rectifiercomprises:${{{\arg \max}_{w}{\sum\limits_{t}{\log \; {p\left( {\left. c_{t} \middle| x_{t} \right.,w} \right)}}}} = {{\arg \max}_{w}{\sum\limits_{t}\frac{1}{1 + {\exp \left( {{- w^{t}}x_{t}} \right)}}}}},$wherein: each x_(t) comprises a t^(th) feature vector; c_(t) comprises acorresponding label; and w comprises a feature weight vector.
 9. Thesystem of claim 1, wherein: the computing instructions are furtherconfigured to run on the one or more processors and perform an act of:training a multi-class classifier on a feed forward neural network toidentify the intent of the user; and determining the intent of the userusing the at least one historical feature vector and the at least onein-session feature vector comprises: determining the intent of the userusing the at least one historical feature vector, the at least onein-session feature vector, and the multi-class classifier.
 10. Thesystem of claim 1, wherein determining the intent of the user using theat least one historical feature vector and the at least one in-sessionfeature vector further comprises: using a set of equations comprising:${{{{{{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)} = {\sum\limits_{i \in }{P\left( {c,\left. i \middle| u_{h} \right.,u_{s},a} \right)}}};} = {\sum\limits_{i \in }{{P\left( {\left. i \middle| u_{h} \right.,u_{s},a} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)}}}};} = {\sum\limits_{i \in }{{P\left( i \middle| u_{s} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},i,a} \right)}}}},$wherein: c comprises a probability of a user having a specific intent;u_(h) comprises at least one historical feature vector; u_(s) comprisesat least one in-session feature vector; i can comprise the intent of theuser; J can comprise an intent of a set of intents; and a can comprisean asset of a set of assets.
 11. A method being implemented viaexecution of computing instructions configured to run at one or moreprocessors and configured to be stored at non-transitorycomputer-readable media, the method comprising: gathering historicaldata comprising interactions of a user with a first graphical userinterface at a first time; storing the historical data comprising theinteractions of the user with the first graphical user interface as atleast one historical feature vector; gathering in-session datacomprising interactions of the user with a second graphical userinterface at a second time later than the first time; storing thein-session data comprising the interactions of the user with the secondgraphical user interface as at least one in-session feature vector;determining an intent of the user using the at least one historicalfeature vector and the at least one in-session feature vector; andtransmitting instructions to display a third graphical user interfacefor the user based upon the intent of the user.
 12. The method of claim11, wherein the in-session data further comprises: a geographicallocation of the user; a date on which the in-session data is gathered;weather patterns at the geographical location of the user; currentevents at the geographical location of the user; or demographics of theuser.
 13. The method of claim 11, wherein the interactions of the userwith the second graphical user interface at the second time later thanthe first time occurs within only one user session.
 14. The method ofclaim 13 further comprising: combining the historical data and thein-session data into second historical data; storing the secondhistorical data as at least one second historical feature vector;gathering second in-session data comprising interactions of the userwith the third graphical user interface at a third time later than thefirst time and the second time; storing the second in-session datacomprising the interactions of the user with the third graphical userinterface as at least one second in-session feature vector; determininga second intent of the user using the at least one second historicalfeature vector and the at least one second in-session feature vector;and transmitting instructions to display a fourth graphical userinterface for the user based upon the second intent of the user.
 15. Themethod of claim 13, wherein: the only one user session begins when theuser logs into an account; and the only one user session ends when theuser logs out of the account.
 16. The method of claim 11, wherein: themethod further comprises: training a binary classifier on a feed forwardneural network to identify the intent of the user; and determining theintent of the user using the at least one historical feature vector andthe at least one in-session feature vector comprises: determining theintent of the user using the at least one historical feature vector, theat least one in-session feature vector, and the binary classifier. 17.The method of claim 16, wherein an output of each node in the feedforward neural network comprises a rectifier having ReLU non-linearity.18. The method of claim 17, wherein the rectifier comprises:${{{\arg \max}_{w}{\sum\limits_{t}{\log \; {p\left( {\left. c_{t} \middle| x_{t} \right.,w} \right)}}}} = {{\arg \max}_{w}{\sum\limits_{t}\frac{1}{1 + {\exp \left( {{- w^{t}}x_{t}} \right)}}}}},$wherein: each x_(t) comprises a t^(th) feature vector; c_(t) comprises acorresponding label; and w comprises a feature weight vector.
 19. Themethod of claim 11, wherein: the method further comprises: training amulti-class classifier on a feed forward neural network to identify theintent of the user; and determining the intent of the user using the atleast one historical feature vector and the at least one in-sessionfeature vector comprises: determining the intent of the user using theat least one historical feature vector, the at least one in-sessionfeature vector, and the multi-class classifier.
 20. The method of claim11, wherein determining the intent of the user using the at least onehistorical feature vector and the at least one in-session feature vectorfurther comprises: using a set of equations comprising:${{{{{{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)} = {\sum\limits_{i \in }{P\left( {c,\left. i \middle| u_{h} \right.,u_{s},a} \right)}}};} = {\sum\limits_{i \in }{{P\left( {\left. i \middle| u_{h} \right.,u_{s},a} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},a} \right)}}}};} = {\sum\limits_{i \in }{{P\left( i \middle| u_{s} \right)}{P\left( {\left. c \middle| u_{h} \right.,u_{s},i,a} \right)}}}},$wherein: c comprises a probability of a user having a specific intent;u_(h) comprises at least one historical feature vector; u_(s) comprisesat least one in-session feature vector; i can comprise the intent of theuser; J can comprise an intent of a set of intents; and a can comprisean asset of a set of assets.